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Research on Authorship Attribution of Article Fragments via RNNs

机译:RNNS文章碎片的作者归因研究

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摘要

Most approaches for authorship attribution are based on traditional machine learning methods. Although these methods are effective, it is difficult for human to choose the features. we present a new model for authorship attribution by using Recurrent Neural Networks(RNNs) because of their powerful abilities of representation and extraction of features. We also use attention mechanism to capture the vital information in the sequence. In addition, a new method for data processing is introduced to meet the needs of the new tasks of authorship attribution. Experimental results show that the approach proposed in this paper is superior to the approach based on traditional machine learning methods.
机译:作者归属的大多数方法都是基于传统的机器学习方法。虽然这些方法是有效的,但是人类难以选择特征。我们通过使用经常性神经网络(RNNS)为其具有强大的表示和特征提取的能力来提出新型号。我们还使用注意机制在序列中捕获重要信息。此外,还引入了一种新的数据处理方法,以满足作者归因的新任务的需求。实验结果表明,本文提出的方法优于基于传统机器学习方法的方法。

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